Suppliers (e.g., manufacturers) and purveyors (e.g., stores, clearinghouses, warehouses, etc.) need to share data. For example, sellers need to order commodities (e.g., items to be sold and/or consumed) with enough precision and certainty that suppliers can respond to the orders with commensurate precision and certainty. Historically, suppliers have dealt with incoming orders using ad hoc techniques involving many ad hoc data formats. In legacy cases, each ordering entity (e.g., a wholesaler, a distribution center, etc.) of products from a particular supplier would provide orders to the supplier, and the supplier would forecast on the basis of received orders. However, such a legacy handling of forecasting based on orders received from entities in the distribution tiers fails to account for point of sale data. Failure to account for point-of-sale data can cause a “bullwhip effect” where a lack of visibility to the point-of-sale demand causes demand variations to become amplified as these variations propagate upstream through the distribution chain to the supplier. Overstatements and understatements of quantities in upstream forecasts (e.g., when the order quantities are greater or less than actually called for by the variations in real consumer demand) tend to result in volatility in the form of inventory build-up and/or shortages at various points in the distribution tiers. What's needed is a technique or techniques to produce accurate forecasts by combining point-of-sale ordering data with ordering data from entities in the distribution tiers.
None of the aforementioned legacy approaches achieve the capabilities of the herein-disclosed techniques for generating consumption-driven forecasts using multi-level heterogeneous input data. Therefore, there is a need for improvements.